基于主元分析神经网络补偿的感应电动机逆解耦控制

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Abstract It is important to realize torque and stator flux dynamic decoupling control for induction motor(IM) drive, which is multi-input and multi-output, nonlinear and strong coupling system. First, an α-order integral inverse model of IM is constructed by the nonlinear state feedback, and the reason that the inverse model accuracy is affected by the feedback error is deeply analyzed. On the base of this, an inverse decoupling control method for IM based on principal component analysis neural network(PCA-NN) compensation is developed, in which the compensated α-order integral inverse model is cascaded with the plant. The complex IM drive system is decoupled into two separate loops, i.e. torque loop and flux loop. The separate loop is respectively designed by linear system theory so that the asymptotic tracking of stator flux and torque is realized. The experimental results verify the validity of the proposed scheme.

Key words： Inverse decoupling control induction motor principal component analysis (PCA) neural network feedback linearization

Received: 29 October 2009 Published: 07 March 2014

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TM351

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	Wu Qinghui
	Lun Shuxian
	Chang Xiaoheng
	Yin Zuoyou

Cite this article:

Wu Qinghui,Lun Shuxian,Chang Xiaoheng等. Inverse Decoupling Control of Induction Motor Based on PCA-NN Compensation[J]. Transactions of China Electrotechnical Society, 2011, 26(1): 40-45.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2011/V26/I1/40

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